13 Publikationen
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2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982807Ullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (2023). Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications. Frontiers in Computational Neuroscience, 17. https://doi.org/10.3389/fncom.2023.1215824
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2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2985715Koravuna, S., Ullah, S., Jungeblut, T., & Rückert, U. (2023). Digit Recognition Using Spiking Neural Networks on FPGA. In I. Rojas, G. Joya, & A. Catala (Eds.), Lecture Notes in Computer Science. Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I (pp. 406-417). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43085-5_32
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2023 | Kurzbeitrag Konferenz / Poster | Veröffentlicht | PUB-ID: 2985713Ullah, S., & Jungeblut, T. (2023). Analysis of MR Images for Early and Accurate Detection of Brain Tumor using Resource Efficient Simulator Brain Analysis. 19th International Conference on Machine Learning and Data Mining MLDM New York USA. https://doi.org/10.5281/zenodo.10457930
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2023 | Kurzbeitrag Konferenz / Poster | Veröffentlicht | PUB-ID: 2985712Ullah, S., Amanullah, A., Roy, K., Lee, J. - A., Chul-Jun, S., & Jungeblut, T. (2023). A Hybrid Spiking-Convolutional Neural Network Approach for Advancing High-Quality Image Inpainting. International Conference on Computer Vision (ICCV) 2023 Paris France . https://doi.org/10.5281/zenodo.10458019
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2023 | Konferenzbeitrag | Angenommen | PUB-ID: 2985188Ullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (Accepted). A Novel Spike Vision Approach for Robust Multi-Object Detection using SNNs. Presented at the Novel Trends in Data Science 2023, Congressi Stefano Franscini at Monte Verità in Ticino, Switzerland. https://doi.org/10.5281/zenodo.10262228
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2023 | Konferenzbeitrag | PUB-ID: 2983660Ullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (2023). Transforming Event-Based into Spike-Rate Datasets for Enhancing Neuronal Behavior Simulation to Bridging the Gap for SNNs. Presented at the International Conference on Computer Vision (ICCV) 2023, Paris France. https://doi.org/10.13140/RG.2.2.14469.32485
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2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982808Ullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (2023). Evaluation of Spiking Neural Nets-Based Image Classification Using the Runtime Simulator RAVSim. International Journal of Neural Systems, 33(09), 2350044. https://doi.org/10.1142/S0129065723500442
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2023 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2982810Ullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (2023). Evaluating Spiking Neural Network Models: A Comparative Performance Analysis. Presented at the . https://doi.org/10.13140/RG.2.2.21295.71847
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2023 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2982811Ullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (2023). Design-Space Exploration of SNN Models using Application-Specific Multi-Core Architectures. Presented at the . https://doi.org/10.13140/RG.2.2.26328.88324
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2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982809Ullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (2023). Streamlined Training of GCN for Node Classification with Automatic Loss Function and Optimizer Selection. In L. Iliadis, I. Maglogiannis, S. Alonso, C. Jayne, & E. Pimenidis (Eds.), Communications in Computer and Information Science. Engineering Applications of Neural Networks. 24th International Conference, EAAAI/EANN 2023, León, Spain, June 14–17, 2023, Proceedings (pp. 191-202). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-34204-2_17
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2022 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2982814Ullah, S., Koravuna, S., Jungeblut, T., & Rückert, U. (2022). Real-Time Resource Efficient Simulator for SNNs-based Model Experimentation. Presented at the . https://doi.org/10.13140/RG.2.2.14584.83201/1
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2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2979461Ullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (2022). SNNs Model Analyzing and Visualizing Experimentation Using RAVSim. In L. Iliadis, C. Jayne, A. Tefas, & E. Pimenidis (Eds.), Communications in Computer and Information Science. Engineering Applications of Neural Networks. 23rd International Conference, EAAAI/EANN 2022, Chersonissos, Crete, Greece, June 17–20, 2022, Proceedings (pp. 40-51). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-08223-8_4
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2022 | Preprint | PUB-ID: 2982804Ullah, S., Koravuna, S., Jungeblut, T., & Rückert, U. (2022). NireHApS: Neuro-Inspired and Resource-Efficient Hardware-Architectures for Plastic SNNs. https://doi.org/10.13140/RG.2.2.16202.85444